CarND-Controls-PID

Self-Driving Car Engineer Nanodegree Program

Dependencies

cmake >= 3.5
All OSes: click here for installation instructions
make >= 4.1(mac, linux), 3.81(Windows)
- Linux: make is installed by default on most Linux distros
- Mac: install Xcode command line tools to get make
- Windows: Click here for installation instructions
gcc/g++ >= 5.4
- Linux: gcc / g++ is installed by default on most Linux distros
- Mac: same deal as make - [install Xcode command line tools]((https://developer.apple.com/xcode/features/)
- Windows: recommend using MinGW
uWebSockets
- Run either ./install-mac.sh or ./install-ubuntu.sh.
- If you install from source, checkout to commit e94b6e1, i.e.
```
git clone https://github.com/uWebSockets/uWebSockets 
cd uWebSockets
git checkout e94b6e1
```
  Some function signatures have changed in v0.14.x. See this PR for more details.
Simulator. You can download these from the project intro page in the classroom.

Fellow students have put together a guide to Windows set-up for the project here if the environment you have set up for the Sensor Fusion projects does not work for this project. There's also an experimental patch for windows in this PR.

Basic Build Instructions

Clone this repo.
Make a build directory: mkdir build && cd build
Compile: cmake .. && make
Run it: ./pid.

Tips for setting up your environment can be found here

Editor Settings

We've purposefully kept editor configuration files out of this repo in order to keep it as simple and environment agnostic as possible. However, we recommend using the following settings:

indent using spaces
set tab width to 2 spaces (keeps the matrices in source code aligned)

Code Style

Please (do your best to) stick to Google's C++ style guide.

Project Instructions and Rubric

Note: regardless of the changes you make, your project must be buildable using cmake and make!

More information is only accessible by people who are already enrolled in Term 2 of CarND. If you are enrolled, see the project page for instructions and the project rubric.

Hints!

You don't have to follow this directory structure, but if you do, your work will span all of the .cpp files here. Keep an eye out for TODOs.

Call for IDE Profiles Pull Requests

Help your fellow students!

We decided to create Makefiles with cmake to keep this project as platform agnostic as possible. Similarly, we omitted IDE profiles in order to we ensure that students don't feel pressured to use one IDE or another.

However! I'd love to help people get up and running with their IDEs of choice. If you've created a profile for an IDE that you think other students would appreciate, we'd love to have you add the requisite profile files and instructions to ide_profiles/. For example if you wanted to add a VS Code profile, you'd add:

/ide_profiles/vscode/.vscode
/ide_profiles/vscode/README.md

The README should explain what the profile does, how to take advantage of it, and how to install it.

Frankly, I've never been involved in a project with multiple IDE profiles before. I believe the best way to handle this would be to keep them out of the repo root to avoid clutter. My expectation is that most profiles will include instructions to copy files to a new location to get picked up by the IDE, but that's just a guess.

One last note here: regardless of the IDE used, every submitted project must still be compilable with cmake and make./

Reflection

In my PID control implementation, I see that the Proportional control parameter (P) corrects the driving position automatically, however because of the linear property of the correction, the car cannot stay stable in the middle which caused problem by an extreme road curve (see the video below)

https://www.youtube.com/watch?v=CLloDodLZ_M

Adding a Differential control parameter (D) solves this problem. This is logically correct because D parameter adjusts the correction with respect to the distance of the car from the reference line. It means that using this the amount of correction will be stronger if the car is far from the reference line. The car can now stay in the line as seen in the video below

https://www.youtube.com/watch?v=PCVIHTZR3ls

I experimentally added an Integration control (I) to the system and saw that the car reacts very quickly to every deviation, which unfortunately leads to overshoot of the car trajectory

https://www.youtube.com/watch?v=1tvPPHexcFY

I used twiddle to automate the parameter optimization. The concept is identical as the one that I learned in the course, however I minimize the needs of collecting the error history by separating the process into sequences where each sequence is called in every steering value calculation

void PID::twiddle(double err) {

    double* K = getKbyId(current_id);
    double* dp = getDpbyId(current_id);

    switch (current_sequence) {
        case 0:
            (*K) += (*dp);
            current_sequence++;
            break;
        case 1:
            if (err < best_err) {
                best_err = err;
                (*dp) *= 1.1;

                // reset
                current_id = (current_id >= 2) ? 0 : (current_id+1);
                current_sequence = 0;
            }

            else {
                (*K) -= 2 * (*dp);
                current_sequence++;
            }
            break;
        case 2:
            if (err < best_err) {
                best_err = err;
                (*dp) *= 1.1;
            }
            else {
                (*K) += (*dp);
                (*dp) *= 0.9;
            }

            // reset
            current_id = (current_id >= 2) ? 0 : (current_id+1);
            current_sequence = 0;
            break;
        default:
            throw ("Sequence must be reset");
    }
}

The final parameters are Kp=0.13165, Ki=0.00236164, Kd=1.79067 and the result can be seen below

https://www.youtube.com/watch?v=df3OAJvaFiY

derzaarsad/PID_control